Agentic Conversations (formally mlops.community)

MLOps Coffee Sessions #141 with Stephen Bailey, Airflow Sucks for MLOps, co-hosted by Joe Reis.

// Abstract
Stephen discusses his experience working with data platforms, particularly the challenges of training and sharing knowledge among different stakeholders. This talk highlights the importance of having clear priorities and a sense of practicality and mentions the use of modular job design and data classification to make it easier for end users to understand which data to use.   
Stephen also mentions the importance of being able to move quickly and not getting bogged down in the quest for perfection. We recommend Stephen's blog post "Airflow's Problem" for further reading.

// Bio
Stephen has worked as a data scientist, analyst, manager, and engineer, and loves all the domains equally. He currently works at Whatnot, a collectibles marketplace that focuses on live shopping, and has previously worked in privacy tech at Immuta. He has his Ph.D. from Vanderbilt University in educational cognitive neuroscience, but it has yet to help him understand why his three children are so crazy.

// MLOps Jobs board

jobs.mlops.community 

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Airflow's Problem blog post: https://stkbailey.substack.com/p/airflows-problem
Airflow's Problem and the reception it got on Hacker News: https://news.ycombinator.com/item?id=32317558

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Joe on LinkedIn: https://www.linkedin.com/in/josephreis/
Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stkbailey/

Timestamps:
[00:00] Stephen's preferred coffee
[00:19] Introduction to co-host Joe Reis
[01:40] Takeaways
[06:29] Subscribe to our newsletters!
[06:55] Shout out to our sponsor, Wallaroo!
[08:05] Whatnot
[10:47] Stephen's side hustle
[14:35] Stephen's work breakdown at Whatnot
[18:03] Fundamental tensions in the data world
[21:27] Initial questions to answer that you were on the right path
[24:06] Recommender systems
[28:15] Coordinating with ML teams
[29:43] Daxter
[31:38] Too advanced, more challenging
[34:37] Orchestration layer
[36:14] Decision criteria
[39:23] Human design aspect of Daxter
[40:53] Orchestration layer centralization and sharing knowledge with stakeholders
[46:18] Airflow's Problem and the reception it got on Hacker News
[51:00] Wrap up

MLOps Coffee Sessions #140 with Sakib Dadi, The Evolution of ML Infrastructure sponsored by Wallaroo.

// Abstract
The toolkit and infrastructure empowering machine learning practitioners are advancing as ML adoption accelerates. We'll go through the current landscape of ML tooling, startups, and new projects from an investor's perspective.

// Bio
Sakib is a vice president in the San Francisco office, where he primarily focuses on early-stage investments in developer platforms, data infrastructure, and machine learning. He has been involved with Bessemer’s investments in Prefect, Coiled, Arcion, Periscope Data (acquired by Sisense), Okera, npm (acquired by GitHub), and LaunchDarkly. Before joining Bessemer, Sakib worked in product at Viagogo, an international marketplace for buying and selling tickets for live events.     
Sakib also worked in the technology investment banking group at Morgan Stanley and as an engineer at Innova Dynamics (acquired by TPK), a startup manufacturing flexible touchscreen displays.

// MLOps Jobs board

jobs.mlops.community  

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Sakib on LinkedIn: https://www.linkedin.com/in/sakib-dadi-77938937/

Timestamps:
[00:00] Sakib's preferred coffee
[00:13] Introduction to Sakib Dadi
[01:33] Sakib's background
[02:40] Shout out to this episode's Sponsor, Wallaroo!
[04:17] ML investing
[05:57] Investing regrets
[08:06] Transformers are today what would be tomorrow?
[09:18] Company that you wish existed now
[10:23] Current thoughts on the MLOps market
[12:32] MLOps transition to Generative AI
[15:52] Mind maps
[17:03] Jasper
[22:14] Intersection  
[24:10] Differences in models in-house
[26:08] Orchestration space
[28:23] Nuances of Monitoring
[29:20] Demetrios' theory on Monitoring
[31:48] Non-funded Monitoring Companies
[34:29] Investment risks
[36:55] Orchestration markets
[39:38] MLOps market at a plateau
[42:14] Vertical problems, vertical solutions  
[45:45] Sakib is starting a company
[50:10] Structuring deals
[51:50] Infrastructure tools
[53:26] Firing a founder  
[53:49] Parting ways with a founder
[57:07] Wrap up

MLOps Coffee Sessions #140 with Sakib Dadi, The Evolution of ML Infrastructure sponsored by Wallaroo.

MLOps Coffee Sessions #139 with Alex Ratner, Putting Foundation Models to Use for the Enterprise, co-hosted by Abi Aryan, sponsored by Snorkel AI.

// Abstract
Foundation models are rightfully being compared to other game-changing industrial advances like steam engines or electric motors. They’re core to the transition of AI from a bespoke, less predictable science to an industrialized, democratized practice. Before they can achieve this impact, however, we need to bridge the cost, quality, and control gaps.

  
Snorkel Flow Foundation Model Suite is the fastest way for AI/ML teams to put foundation models to use. For some projects, this means fine-tuning a foundation model for production dramatically faster by creating programmatically labeling training data. For others, the optimal solution will be using Snorkel Flow’s distill, combine, and correct approach to extract the most relevant knowledge from foundation models and encode that value into the right-sized models for your use case.   
AI/ML teams can determine which Foundation Model Suite capabilities to use (and in what combination) to optimize for cost, quality, and control using Snorkel Flow’s integrated workflow for programmatic labeling, model training, and rapid-guided iteration.

// Bio
Alex Ratner is the Co-founder and CEO of Snorkel AI and an Assistant Professor of Computer Science at the University of Washington.

  
Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open-source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.

// MLOps Jobs board  
jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: www.snorkel.ai
Huge “foundation models” are turbo-charging AI progress: https://www.economist.com/interactive/briefing/2022/06/11/huge-foundation-models-are-turbo-charging-ai-progress
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming: https://arxiv.org/abs/2203.01382
The Principles of Data-Centric AI Development: https://snorkel.ai/principles-of-data-centric-ai-development/

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/
Connect with Alex on LinkedIn: https://www.linkedin.com/in/alexander-ratner-038ba239/

Timestamps:

[00:00] Alex's preferred coffee

[01:20] Introduction to Alex Ratner

[02:34] Takeaways

[04:04] Huge shoutout to our Sponsor, Snorkel AI!

[04:39] Comment, rate us, and share our podcasts with your friends!  

[04:50] Transfer Learning / Active Learning

[11:30] Labeling Heuristics paper on Nemo

[18:14] Geocentric AI

[21:48] Enterprise use cases on Foundational Models

[32:45] Foundational Models in the different Google products

[38:36] Progress in Foundational Models

[43:55] AutoML Models Baseline Accuracy

[44:40] Hosting Infrastructure Snorkel Float vs GCP

[46:53] Chris Re's venture capital firm/incubator/machine

[51:00] Wrap

MLOps Coffee Sessions #138 with Dattaraj Rao, Explainability in the MLOps Cycle, co-hosted by Vishnu Rachakonda.

// Abstract
When it comes to Dattaraj's interests, you'll hear about his top 3 areas in Machine Learning. What he sees as up and coming, what he's investing his company's time into, and where he invests his own time.
Learn more about rule-based systems, deploying rule-based systems, and how to incorporate systems into more systems. There is no difference between ML systems and deploying models. It's just that this machine learning model is much smarter than traditional rule-based models.

// Bio
Dattaraj Jagdish Rao is the author of the book “Keras to Kubernetes: The Journey of a Machine Learning Model to Production”. Dattaraj leads the AI Research Lab at Persistent and is responsible for driving thought leadership in AI/ML across the company. He leads a team that explores state-of-the-art algorithms in Knowledge Graphs, NLU, Responsible AI, MLOps, and demonstrates applicability in Healthcare, Banking, and Industrial domains. Earlier, he worked at General Electric (GE) for 19 years, building Industrial IoT solutions for Predictive Maintenance, Digital Twins, and Machine Vision.
Dattaraj held several Technology Leadership roles at Global Research, GE Power, and Transportation (now part of Wabtec). He led the Innovation team out of Bangalore that incubated video track inspection from an idea into a commercial Product. Dattaraj has 11 patents in the Machine Learning and Computer Vision areas.

// MLOps Jobs board

jobs.mlops.community  

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Keras to Kubernetes: The Journey of a Machine Learning Model to Production book:
https://www.amazon.com/Keras-Kubernetes-Journey-Learning-Production/dp/1119564832
Responsible Data Science Research | Talk @ VLDB 2022| Dattaraj Rao
https://www.youtube.com/watch?v=5_19KvSiy8s
Operationalizing AI/ML: Journey of an ML Model to Production | Masterclass by Dattaraj Rao
https://www.youtube.com/watch?v=Zk3RiiG07Us
Dattaraj Rao presenting workshop on MLOps at VISUM 2021
https://www.youtube.com/watch?v=wonUvbMDTUA
Machine Learning Design Patterns book:
https://www.oreilly.com/library/view/machine-learning-design/9781098115777/

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Dattaraj on LinkedIn: https://www.linkedin.com/in/dattarajrao/

Timestamps:

[00:00] Dattaraj's preferred coffee

[01:12] Introduction to Dattaraj Rao

[02:57] Takeaways

[05:10] This podcast is brought to you by Superwise!

[06:10] Dattaraj's background

[12:37] Top 3 interests of Dattaraj

[16:23] Examples of Large Language Models use cases are not a good application

[21:44] Future of Large Language Models - change or inherent problem

[23:12] Remote Monitoring and Diagnostic

[29:25] Keras to Kubernetes book

[33:44] Dattaraj's title for his next book

[37:12] Machine Learning Design Patterns to keep in mind

[43:10] Model registries and multi-tenancy

[44:49] Wrap up

MLOps Coffee Sessions #137 with Niklas Kühl, Machine Learning Operations — What is it and Why Do We Need It? co-hosted by Abi Aryan.

// Abstract
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production.   
However, it is highly challenging to automate and operationalize ML products, and thus, many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue.

// Bio
NIKLAS KÜHL studied Industrial Engineering & Management at the Karlsruhe Institute of Technology (KIT) (Bachelor's and Master's). During his studies, he gained practical experience in IT by working at Porsche in both national and international roles. Niklas has been working on machine learning (ML) and artificial intelligence (AI) in different domains since 2014. In 2017, he gained his PhD (summa cum laude) in Information Systems with a focus on applied machine learning from KIT. In 2020, he joined IBM.

As of today, Niklas engages in two complementary roles: He is head of the Applied AI in Services Lab at the Karlsruhe Institute of Technology (KIT), and, furthermore, he works as a Managing Consultant for Data Science at IBM. In his academic and practical projects, he is working on conceptualizing, designing, and implementing AI in Systems with a focus on robust and fair AI as well as the effective collaboration between users and intelligent agents. Currently, he and his team are actively working on different ML & AI solutions within industrial services, sales forecasting, production lines or even creativity. Niklas is internationally collaborating with multiple institutions, like the University of Texas and the MIT-IBM Watson AI Lab.

// MLOps Jobs board

jobs.mlops.community 
// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: niklas.xyz
MLOps Newsletters: https://airtable.com/shrx9X19pGTWa7U3Y
Machine Learning Operations (MLOps): Overview, Definition, and Architecture paper: https://arxiv.org/abs/2205.02302

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/
Connect with Niklas on LinkedIn: https://www.linkedin.com/in/niklaskuehl/

Timestamps:
[00:00] Niklas' preferred coffee
[00:43] Introduction to Niklas Kühl
[01:16] Takeaways
[02:05] Subscribe to our newsletters and give us a rating here!
[02:54] Niklas background
[05:09] Scraping Twitter data
[06:58] EV's conclusions
[08:24] NLP usage on Twitter
[10:26] Consumer behavior production
[12:03] Management and Machine Learning Systems Communication
[14:00] Current hype around Machine Learning
[15:10] Budgeting ML Productions
[18:15] Machine Learning Operations (MLOps): Overview, Definition, and Architecture paper
[22:56] Niklas' MLOps definiton  
[25:55] Navigating the idea of MLOps
[30:34] Return on Investment endeavor
[33:58] Full-stack data scientist
[37:39] Defining success for different kinds of data science projects

[41:06] Fun fact about Niklas

[44:35] Other things Niklas does

[47:02] The world is your oyster

[50:57] Niklas' day-to-day life

[52:48] One lecture Niklas can drop in

[53:57] Foundational models

[58:20] Wrap up

MLOps Coffee Sessions #136 with Andrew Dye, Systems Engineer, Navigating the World of ML, co-hosted by David Aponte.

// Abstract
We don't hear that much about working at a very low level on this podcast, but they are still very valid. Andrew is able to give us his take on why and what you need to keep in mind when you are working at these low levels, and why it is very important when you are a Machine Learning Engineer, and how the two can play together nicely.
Most MLOps teams are formed using existing people and existing engineers. More often than not, you have to blend these various disciplines, and it works well when there's a common goal.

// Bio
Andrew is a software engineer at Union and a contributor to Flyte, a production-grade data and ML orchestration platform. Prior to that, he was a tech lead for ML Infrastructure at Meta, where he focused on ML training reliability.

// MLOps Jobs board

jobs.mlops.community  

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrewwdye

Timestamps:
[00:00] Andrew's preferred coffee
[03:30] Introduction to Andrew Dye
[03:33] Takeaways
[07:32] Huge shoutout to our sponsors UnionML and UnionAI!
[07:48] Andrew's background
[10:08] Andrew's learning curve
[11:10] Bridging the gap between firmware space and MLOps
[12:18] In connection with the Pytorch team
[12:54] Things that should have been learned sooner
[14:54] Type of scale Andrew works on
[17:42] Distributed training at Meta
[19:55] Managing the huge search space
[22:18] Execution patterns programs
[23:20] Non-ML engineers dealing with ML engineers having the same skill set
[26:44] Pace rapid change adoption
[29:18] Consensus challenges
[32:26] Abstractions making sense now
[34:53] Comparing to others
[39:21] General principles in UnionAI tooling
[41:54] Seeing the future
[43:54] Inter-task checkpointing
[44:52] Combining functionality with use cases
[46:17] Wrap up

MLOps Coffee Sessions #135 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference, co-hosted by Skylar Payne.  

// Abstract
Moving from batch/offline Machine Learning to more interactive "near" real-time requires knowledge, team, planning, and effort. We discuss what it means to do real-time inference and near-real-time features, when to make this move, what tools to use, and what steps to take.  

// Bio
Sasha Ovsankin

Sasha is currently a Tech Lead of Machine Learning Model Serving infrastructure at LinkedIn, worked also on Feathr Feature Store, Real-Time Feature pipelines, designed metric platforms at LinkedIn and Uber, and was a co-founder in two startups. Sasha is passionate about AI, Software Craftsmanship, improvisational music, and many other things.  

Rupesh Gupta
Rupesh is a Sr. Staff Engineer in the AI team at LinkedIn. He has 10 years of experience in search and recommender systems.  

// MLOps Jobs board  

jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links  

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Sasha on LinkedIn: https://www.linkedin.com/in/sashao/
Connect with Rupesh on LinkedIn: https://www.linkedin.com/in/guptarupesh

Timestamps:
[00:00] Sasha's and Rupesh's preferred coffee
[01:30] Takeaways
[07:23] Changes in LinkedIn
[09:21] "Real-time" Machine Learning in LinkedIn
[13:08] Value of Feedback
[14:24] Technical details behind getting the most recent information integrated into the models
[16:53] Embedding Vector Search action occurrence
[18:33] Meaning of "Real-time" Features and Inference
[20:23] Are "Real-time" Features always worth that effort and always helpful?
[23:22] Importance of model application
[25:26] Challenges in "Real-time" Features
[30:40] System design review on Pinterest
[36:13] Successes of real-time features
[38:31] Learnings to share
[45:52] Branching for Machine Learning
[48:44] Not so talked about discussion of "Real-time"
[51:09] Wrap up

MLOps Coffee Sessions #134 with Jeremy Thomas Jordan, Building Threat Detection Systems: An MLE's Perspective, co-hosted by Vishnu Rachakonda.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

// Abstract
There is a clear pattern that we have been seeing with some of these greats in MLOps. So many use writing as a forcing function to learn about where they have holes in their understanding of something.   
If you are not writing, this episode is important as to why writing is important for your own development. Jeremy goes into writing in depth as to how beneficial it is for him to write and for him to see that he doesn't understand something if he cannot re-articulate it in writing.

// Bio
Jeremy is a machine learning engineer currently working at Duo Security, where he focuses on building ML infrastructure to operate threat detection systems at scale. He previously worked at Proofpoint, where he built models for phishing and malware detection.

// MLOps Jobs board

jobs.mlops.community  

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links Website:
https://www.jeremyjordan.me/

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Visnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Jeremy on Twitter: https://twitter.com/jeremyjordan

Timestamps:

[00:00] Jeremy's preferred coffee

[00:15] Introduction of hosts

[00:34] Introduction to Jeremy Thomas Jordan

[01:11] Takeaways

[04:50] Like, subscribe, and leave a review on our platforms

[05:55] Normcore Conference

[09:45] When are rule engines good and when are they not?

[11:50] Going with Implemental Rule

[12:13] Implementing a Rule

[14:42] Managing rules sprawl

[16:40] Reviewing and updating the rules

[17:35] Chaining

[19:27] Rules catching complicated threats

[24:47] Notion of risk and confidence

[26:38] Effective testing for machine learning systems blog post

[28:51] Testing large language models

[31:03] Model evaluation vs model testing

[34:08] Approach to fundamentals

[36:51] “The Cobbler’s Children Have No Shoes”

[41:39] Writing is a checkpoint for Jeremy

[43:10] Posting Jeremy's blogs on https://ghost.org/

[44:32] Jeremy's blogs distribution platforms

[46:18] Jeremy's most popular blog post

[48:14] Sign up for NormConf for free here https://normconf.com/ and attend on December 15th!

[49:50] Wrap up

MLOps Coffee Sessions #134 with Sasha Ovsankin and Rupesh Gupta, Real-time Machine Learning: Features and Inference co-hosted by Skylar Payne.